4798 papers
This paper presents a white-box support vector machine framework optimized by five meta-heuristic swarm algorithms to monitor the health of toothed milling cutters in real-time by characterizing spindle vibrations and selecting relevant statistical features through Recursive Feature Elimination with Cross-Validation.
This paper provides a comprehensive overview of Automated Reinforcement Learning (AutoRL), surveying existing literature including recent LLM-based techniques, discussing promising non-tailored methods for future integration, and outlining current challenges and research directions in automating MDP modeling, algorithm selection, and hyper-parameter optimization.
This paper proposes an uncertainty-aware, explainable-by-design subsequence-based model that achieves state-of-the-art classification performance for astronomical uncertain time series by incorporating data uncertainty as an input, thereby enabling domain experts to inspect predictions and potentially inspire new theoretical astrophysics developments.
This paper introduces an ensemble Gaussian Process Regression method that significantly reduces computational complexity while outperforming existing machine learning models in predicting US stock returns and constructing superior mean-variance optimal portfolios based on prediction uncertainty.
This paper establishes finite-sample guarantees for a cost-driven representation learning method that learns a latent dynamic model by predicting multi-step costs, enabling the derivation of near-optimal controllers for finite-horizon Linear Quadratic Gaussian (LQG) control problems without explicitly modeling observations or actions.
This paper introduces the IW-DKF, a deep Kalman filter framework enhanced by importance sampling, which demonstrates improved generative modeling performance and more accurate state and parameter estimation for non-linear physics-based models compared to standard variational approaches.
This paper proposes MU-Mis, a remaining-data-free machine unlearning method that achieves performance comparable to data-dependent approaches by theoretically linking sample contribution to model sensitivity and directly suppressing this sensitivity using only the pre-trained model.
This paper presents a machine learning-focused survey of Computerized Adaptive Testing (CAT), exploring how ML techniques can optimize measurement models, question selection, bank construction, and test control to create more robust, fair, and efficient adaptive assessment systems across various domains.
This paper introduces the NQS-DQME framework, which encodes environmental memory into dissipaton quasiparticles to enable neural quantum states to efficiently and accurately simulate non-Markovian open quantum dynamics with improved scalability and interpretability.
The paper introduces LoRA-Ensemble, a parameter-efficient method that leverages Low-Rank Adaptation to create an implicit ensemble for self-attention networks, achieving superior calibration and accuracy comparable to explicit ensembles while significantly reducing computational and memory costs.
This paper introduces Fast Explanation (FEX), a novel framework that utilizes policy gradient optimization to represent attribution-based explanations as probability distributions, achieving over 97% reduction in inference time and 70% less memory usage compared to traditional model-agnostic methods while maintaining high-quality, scalable explanations for image and text classification tasks.
This paper identifies a "corruption stage" in few-shot fine-tuned diffusion models caused by a narrowed learning distribution and proposes a Bayesian Neural Network approach with variational inference to broaden this distribution, thereby mitigating corruption and improving image fidelity, quality, and diversity without additional inference costs.
This paper proposes DKDL-Net, a lightweight bearing fault detection model that combines decoupled knowledge distillation and Low-Rank Adaptation fine-tuning to achieve state-of-the-art accuracy (99.48%) with significantly reduced computational complexity and parameter count compared to existing methods.
This paper proposes a structured semiparametric framework combining an algorithm choice model and a viewer response model to correct the severe bias in standard estimators caused by algorithmic interference in two-sided marketplaces, thereby enabling accurate global treatment effect estimation for platform-wide algorithm updates.
LAMBDA is a novel, open-source, code-free multi-agent system that leverages large language models with collaborative programmer and inspector roles, along with a knowledge integration mechanism and user intervention capabilities, to enhance the accessibility and efficiency of data analysis for diverse users.
The paper proposes OTAD, a novel two-step defense framework that combines optimal transport theory with convex integration to train deep neural networks that achieve both high accuracy and certified local Lipschitz robustness against adversarial attacks.
This paper establishes the statistical foundations of the mini-batch maximum partial-likelihood estimator (mb-MPLE) for deep Cox models optimized via stochastic gradient descent, proving its consistency and optimal convergence rates while providing practical guidance on hyperparameter tuning and demonstrating its effectiveness in large-scale applications where standard estimation is intractable.
This paper proposes a novel Variational Learning framework for Gaussian Process Latent Variable Models that utilizes Stochastic Gradient Annealed Importance Sampling to overcome proposal distribution challenges in high-dimensional spaces, achieving tighter variational bounds and superior performance compared to state-of-the-art methods.
This paper introduces the -Gromov-Wasserstein distance as a unified framework for comparing -networks (measure spaces with -valued kernels), proving that it forms a metric space with desirable topological properties while providing computable bounds for practical applications.
This paper cautions against treating post hoc explainers like SHAP and LIME as definitive evidence for underlying data relationships in business research, demonstrating through a systematic review and simulation that their explanations often misalign with true data-generating processes due to feature correlation and the Rashomon effect, and thus should be used only as exploratory tools rather than for hypothesis validation.